Text-to-SQL webinar

It's been a while since my last update. I've been heads-down working on various GenAI projects, from early prototypes to full production systems. Throughout all this work, I've spent a lot of time focusing on one particular problem: getting LLMs to translate everyday questions into SQL queries. After plenty of trial and error, I've learned some valuable lessons that I think are worth sharing.

I've put together a quick overview below, but if you want the full story, I'm running a webinar next week to dive deeper.

Text-to-SQL top challenges:

🔸 Hallucinations: Models referencing non-existent tables and columns
🔸 Query Accuracy: Syntactically perfect SQL producing incorrect results
🔸 Consistency Issues: Varying responses to identical questions
🔸 Question Ambiguity: Handling unclear or imprecise user inputs
🔸 Performance Bottlenecks: Slow execution times in AI agent operations
🔸 Security Concerns: LLM-based SQL injections

I'll address these and some other questions in the webinar:

🔹 Handling extensive database schemas? Yes
🔹 Managing company-specific terminology? Simple
🔹 Value of larger context windows? Less than you'd think
🔹 LLM fine-tuning necessity? Rarely needed
🔹 Model interchangeability (OpenAI to Anthropic to Gemini to DeepSeek)? I've got proven solutions

📅 Want the details? Join me for a one-hour live session next week.

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See you soon,

Dima